Overview

Dataset statistics

Number of variables26
Number of observations400
Missing cells1009
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.4 KiB
Average record size in memory208.3 B

Variable types

NUM12
CAT11
BOOL3

Warnings

wc has a high cardinality: 92 distinct values High cardinality
age has 9 (2.2%) missing values Missing
bp has 12 (3.0%) missing values Missing
sg has 47 (11.8%) missing values Missing
al has 46 (11.5%) missing values Missing
su has 49 (12.3%) missing values Missing
rbc has 152 (38.0%) missing values Missing
pc has 65 (16.2%) missing values Missing
pcc has 4 (1.0%) missing values Missing
ba has 4 (1.0%) missing values Missing
bgr has 44 (11.0%) missing values Missing
bu has 19 (4.7%) missing values Missing
sc has 17 (4.2%) missing values Missing
sod has 87 (21.8%) missing values Missing
pot has 88 (22.0%) missing values Missing
hemo has 52 (13.0%) missing values Missing
pcv has 70 (17.5%) missing values Missing
wc has 105 (26.2%) missing values Missing
rc has 130 (32.5%) missing values Missing
id has unique values Unique
al has 199 (49.8%) zeros Zeros
su has 290 (72.5%) zeros Zeros

Reproduction

Analysis started2020-12-12 05:31:15.241318
Analysis finished2020-12-12 05:32:12.580345
Duration57.34 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

UNIQUE

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.5
Minimum0
Maximum399
Zeros1
Zeros (%)0.2%
Memory size3.1 KiB
2020-12-12T11:02:12.785185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.95
Q199.75
median199.5
Q3299.25
95-th percentile379.05
Maximum399
Range399
Interquartile range (IQR)199.5

Descriptive statistics

Standard deviation115.6143013
Coefficient of variation (CV)0.5795203073
Kurtosis-1.2
Mean199.5
Median Absolute Deviation (MAD)100
Skewness0
Sum79800
Variance13366.66667
MonotocityStrictly increasing
2020-12-12T11:02:13.079999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
39910.2%
 
13610.2%
 
12610.2%
 
12710.2%
 
12810.2%
 
12910.2%
 
13010.2%
 
13110.2%
 
13210.2%
 
13310.2%
 
Other values (390)39097.5%
 
ValueCountFrequency (%) 
010.2%
 
110.2%
 
210.2%
 
310.2%
 
410.2%
 
ValueCountFrequency (%) 
39910.2%
 
39810.2%
 
39710.2%
 
39610.2%
 
39510.2%
 

age
Real number (ℝ≥0)

MISSING

Distinct76
Distinct (%)19.4%
Missing9
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean51.48337596
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-12T11:02:13.384387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q142
median55
Q364.5
95-th percentile74.5
Maximum90
Range88
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation17.16971409
Coefficient of variation (CV)0.3335001594
Kurtosis0.0578404946
Mean51.48337596
Median Absolute Deviation (MAD)10
Skewness-0.6682594692
Sum20130
Variance294.7990819
MonotocityNot monotonic
2020-12-12T11:02:13.626376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
60194.8%
 
65174.2%
 
48123.0%
 
50123.0%
 
55123.0%
 
47112.8%
 
62102.5%
 
56102.5%
 
45102.5%
 
54102.5%
 
Other values (66)26867.0%
 
ValueCountFrequency (%) 
210.2%
 
310.2%
 
410.2%
 
520.5%
 
610.2%
 
ValueCountFrequency (%) 
9010.2%
 
8310.2%
 
8210.2%
 
8110.2%
 
8041.0%
 

bp
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)2.6%
Missing12
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean76.46907216
Minimum50
Maximum180
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-12T11:02:13.916938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median80
Q380
95-th percentile100
Maximum180
Range130
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.68363749
Coefficient of variation (CV)0.1789434226
Kurtosis8.646095189
Mean76.46907216
Median Absolute Deviation (MAD)10
Skewness1.605428957
Sum29670
Variance187.2419351
MonotocityNot monotonic
2020-12-12T11:02:14.199992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8011629.0%
 
7011228.0%
 
607117.8%
 
905313.2%
 
100256.2%
 
5051.2%
 
11030.8%
 
12010.2%
 
18010.2%
 
14010.2%
 
(Missing)123.0%
 
ValueCountFrequency (%) 
5051.2%
 
607117.8%
 
7011228.0%
 
8011629.0%
 
905313.2%
 
ValueCountFrequency (%) 
18010.2%
 
14010.2%
 
12010.2%
 
11030.8%
 
100256.2%
 

sg
Real number (ℝ≥0)

MISSING

Distinct5
Distinct (%)1.4%
Missing47
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean1.017407932
Minimum1.005
Maximum1.025
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-12T11:02:14.696853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.005
5-th percentile1.01
Q11.01
median1.02
Q31.02
95-th percentile1.025
Maximum1.025
Range0.02
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.005716616974
Coefficient of variation (CV)0.005618805196
Kurtosis-1.144356928
Mean1.017407932
Median Absolute Deviation (MAD)0.005
Skewness-0.1724437507
Sum359.145
Variance3.267970963e-05
MonotocityNot monotonic
2020-12-12T11:02:14.871452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
1.0210626.5%
 
1.018421.0%
 
1.0258120.2%
 
1.0157518.8%
 
1.00571.8%
 
(Missing)4711.8%
 
ValueCountFrequency (%) 
1.00571.8%
 
1.018421.0%
 
1.0157518.8%
 
1.0210626.5%
 
1.0258120.2%
 
ValueCountFrequency (%) 
1.0258120.2%
 
1.0210626.5%
 
1.0157518.8%
 
1.018421.0%
 
1.00571.8%
 

al
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)1.7%
Missing46
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean1.016949153
Minimum0
Maximum5
Zeros199
Zeros (%)49.8%
Memory size3.1 KiB
2020-12-12T11:02:15.081595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.352678913
Coefficient of variation (CV)1.330134264
Kurtosis-0.3833766021
Mean1.016949153
Median Absolute Deviation (MAD)0
Skewness0.9981572421
Sum360
Variance1.829740241
MonotocityNot monotonic
2020-12-12T11:02:15.256633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
019949.8%
 
14411.0%
 
34310.8%
 
24310.8%
 
4246.0%
 
510.2%
 
(Missing)4611.5%
 
ValueCountFrequency (%) 
019949.8%
 
14411.0%
 
24310.8%
 
34310.8%
 
4246.0%
 
ValueCountFrequency (%) 
510.2%
 
4246.0%
 
34310.8%
 
24310.8%
 
14411.0%
 

su
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)1.7%
Missing49
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean0.4501424501
Minimum0
Maximum5
Zeros290
Zeros (%)72.5%
Memory size3.1 KiB
2020-12-12T11:02:15.425909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.099191252
Coefficient of variation (CV)2.441874237
Kurtosis5.055348003
Mean0.4501424501
Median Absolute Deviation (MAD)0
Skewness2.464261823
Sum158
Variance1.208221408
MonotocityNot monotonic
2020-12-12T11:02:15.596945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
029072.5%
 
2184.5%
 
3143.5%
 
1133.2%
 
4133.2%
 
530.8%
 
(Missing)4912.2%
 
ValueCountFrequency (%) 
029072.5%
 
1133.2%
 
2184.5%
 
3143.5%
 
4133.2%
 
ValueCountFrequency (%) 
530.8%
 
4133.2%
 
3143.5%
 
2184.5%
 
1133.2%
 

rbc
Categorical

MISSING

Distinct2
Distinct (%)0.8%
Missing152
Missing (%)38.0%
Memory size3.1 KiB
normal
201 
abnormal
47 
ValueCountFrequency (%) 
normal20150.2%
 
abnormal4711.8%
 
(Missing)15238.0%
 
2020-12-12T11:02:15.794097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T11:02:15.915236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:16.068761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length6
Mean length5.095
Min length3

pc
Categorical

MISSING

Distinct2
Distinct (%)0.6%
Missing65
Missing (%)16.2%
Memory size3.1 KiB
normal
259 
abnormal
76 
ValueCountFrequency (%) 
normal25964.8%
 
abnormal7619.0%
 
(Missing)6516.2%
 
2020-12-12T11:02:16.273071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T11:02:16.423208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:16.581415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length6
Mean length5.8925
Min length3

pcc
Categorical

MISSING

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.1 KiB
notpresent
354 
present
42 
ValueCountFrequency (%) 
notpresent35488.5%
 
present4210.5%
 
(Missing)41.0%
 
2020-12-12T11:02:16.755811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T11:02:16.883965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:17.084895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.615
Min length3

ba
Categorical

MISSING

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.1 KiB
notpresent
374 
present
 
22
ValueCountFrequency (%) 
notpresent37493.5%
 
present225.5%
 
(Missing)41.0%
 
2020-12-12T11:02:17.308214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T11:02:17.433860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:17.585405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.765
Min length3

bgr
Real number (ℝ≥0)

MISSING

Distinct146
Distinct (%)41.0%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean148.0365169
Minimum22
Maximum490
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-12T11:02:17.806941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78.75
Q199
median121
Q3163
95-th percentile307.25
Maximum490
Range468
Interquartile range (IQR)64

Descriptive statistics

Standard deviation79.28171424
Coefficient of variation (CV)0.5355551179
Kurtosis4.225593588
Mean148.0365169
Median Absolute Deviation (MAD)25
Skewness2.010773173
Sum52701
Variance6285.590212
MonotocityNot monotonic
2020-12-12T11:02:18.073422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
99102.5%
 
9392.2%
 
10092.2%
 
10782.0%
 
13161.5%
 
9261.5%
 
10961.5%
 
14061.5%
 
13061.5%
 
11761.5%
 
Other values (136)28471.0%
 
(Missing)4411.0%
 
ValueCountFrequency (%) 
2210.2%
 
7051.2%
 
7430.8%
 
7520.5%
 
7641.0%
 
ValueCountFrequency (%) 
49020.5%
 
46310.2%
 
44710.2%
 
42510.2%
 
42420.5%
 

bu
Real number (ℝ≥0)

MISSING

Distinct118
Distinct (%)31.0%
Missing19
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean57.42572178
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-12T11:02:18.320240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median42
Q366
95-th percentile162
Maximum391
Range389.5
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.50300585
Coefficient of variation (CV)0.8794492133
Kurtosis9.345288576
Mean57.42572178
Median Absolute Deviation (MAD)16
Skewness2.634374459
Sum21879.2
Variance2550.5536
MonotocityNot monotonic
2020-12-12T11:02:18.574242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
46153.8%
 
25133.2%
 
19112.8%
 
40102.5%
 
1892.2%
 
1592.2%
 
4892.2%
 
5092.2%
 
3282.0%
 
2682.0%
 
Other values (108)28070.0%
 
(Missing)194.8%
 
ValueCountFrequency (%) 
1.510.2%
 
1020.5%
 
1592.2%
 
1671.8%
 
1771.8%
 
ValueCountFrequency (%) 
39110.2%
 
32210.2%
 
30910.2%
 
24110.2%
 
23510.2%
 

sc
Real number (ℝ≥0)

MISSING

Distinct84
Distinct (%)21.9%
Missing17
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.072454308
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-12T11:02:18.936647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.3
Q32.8
95-th percentile11.89
Maximum76
Range75.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation5.741126067
Coefficient of variation (CV)1.868579803
Kurtosis79.30434545
Mean3.072454308
Median Absolute Deviation (MAD)0.6
Skewness7.509538252
Sum1176.75
Variance32.96052852
MonotocityNot monotonic
2020-12-12T11:02:19.436690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.24010.0%
 
1.1246.0%
 
1235.8%
 
0.5235.8%
 
0.7225.5%
 
0.9225.5%
 
0.6184.5%
 
0.8174.2%
 
2.2102.5%
 
1.792.2%
 
Other values (74)17543.8%
 
(Missing)174.2%
 
ValueCountFrequency (%) 
0.410.2%
 
0.5235.8%
 
0.6184.5%
 
0.7225.5%
 
0.8174.2%
 
ValueCountFrequency (%) 
7610.2%
 
48.110.2%
 
3210.2%
 
2410.2%
 
18.110.2%
 

sod
Real number (ℝ≥0)

MISSING

Distinct34
Distinct (%)10.9%
Missing87
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean137.528754
Minimum4.5
Maximum163
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-12T11:02:19.809126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile125
Q1135
median138
Q3142
95-th percentile150
Maximum163
Range158.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.40875205
Coefficient of variation (CV)0.07568418785
Kurtosis85.53436962
Mean137.528754
Median Absolute Deviation (MAD)3
Skewness-6.996568561
Sum43046.5
Variance108.3421193
MonotocityNot monotonic
2020-12-12T11:02:20.056888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%) 
1354010.0%
 
140256.2%
 
141225.5%
 
139215.2%
 
142205.0%
 
138205.0%
 
137194.8%
 
150174.2%
 
136174.2%
 
147133.2%
 
Other values (24)9924.8%
 
(Missing)8721.8%
 
ValueCountFrequency (%) 
4.510.2%
 
10410.2%
 
11110.2%
 
11320.5%
 
11420.5%
 
ValueCountFrequency (%) 
16310.2%
 
150174.2%
 
147133.2%
 
146102.5%
 
145112.8%
 

pot
Real number (ℝ≥0)

MISSING

Distinct40
Distinct (%)12.8%
Missing88
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean4.62724359
Minimum2.5
Maximum47
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-12T11:02:20.306927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.4
Q13.8
median4.4
Q34.9
95-th percentile5.7
Maximum47
Range44.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation3.193904177
Coefficient of variation (CV)0.6902390407
Kurtosis142.5059115
Mean4.62724359
Median Absolute Deviation (MAD)0.5
Skewness11.58295556
Sum1443.7
Variance10.20102389
MonotocityNot monotonic
2020-12-12T11:02:20.548272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%) 
5307.5%
 
3.5307.5%
 
4.9276.8%
 
4.7174.2%
 
4.8164.0%
 
4.4143.5%
 
3.8143.5%
 
4.2143.5%
 
4.1143.5%
 
3.9143.5%
 
Other values (30)12230.5%
 
(Missing)8822.0%
 
ValueCountFrequency (%) 
2.520.5%
 
2.710.2%
 
2.810.2%
 
2.930.8%
 
320.5%
 
ValueCountFrequency (%) 
4710.2%
 
3910.2%
 
7.610.2%
 
6.610.2%
 
6.520.5%
 

hemo
Real number (ℝ≥0)

MISSING

Distinct115
Distinct (%)33.0%
Missing52
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12.52643678
Minimum3.1
Maximum17.8
Zeros0
Zeros (%)0.0%
Memory size3.1 KiB
2020-12-12T11:02:20.801999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile7.9
Q110.3
median12.65
Q315
95-th percentile16.9
Maximum17.8
Range14.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.912586609
Coefficient of variation (CV)0.2325151725
Kurtosis-0.4713980437
Mean12.52643678
Median Absolute Deviation (MAD)2.35
Skewness-0.3350946792
Sum4359.2
Variance8.483160754
MonotocityNot monotonic
2020-12-12T11:02:21.084060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15164.0%
 
10.982.0%
 
1371.8%
 
13.671.8%
 
9.871.8%
 
11.171.8%
 
1261.5%
 
13.961.5%
 
11.361.5%
 
10.361.5%
 
Other values (105)27268.0%
 
(Missing)5213.0%
 
ValueCountFrequency (%) 
3.110.2%
 
4.810.2%
 
5.510.2%
 
5.610.2%
 
5.810.2%
 
ValueCountFrequency (%) 
17.830.8%
 
17.710.2%
 
17.610.2%
 
17.510.2%
 
17.420.5%
 

pcv
Categorical

MISSING

Distinct44
Distinct (%)13.3%
Missing70
Missing (%)17.5%
Memory size3.1 KiB
52
 
21
41
 
21
44
 
19
48
 
19
40
 
16
Other values (39)
234 
ValueCountFrequency (%) 
52215.2%
 
41215.2%
 
44194.8%
 
48194.8%
 
40164.0%
 
43143.5%
 
45133.2%
 
42133.2%
 
33123.0%
 
50123.0%
 
Other values (34)17042.5%
 
(Missing)7017.5%
 
2020-12-12T11:02:21.408957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique10 ?
Unique (%)3.0%
2020-12-12T11:02:21.647330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.175
Min length1

wc
Categorical

HIGH CARDINALITY
MISSING

Distinct92
Distinct (%)31.2%
Missing105
Missing (%)26.2%
Memory size3.1 KiB
9800
 
11
6700
 
10
9200
 
9
9600
 
9
7200
 
9
Other values (87)
247 
ValueCountFrequency (%) 
9800112.8%
 
6700102.5%
 
920092.2%
 
960092.2%
 
720092.2%
 
1100082.0%
 
690082.0%
 
580082.0%
 
700071.8%
 
910071.8%
 
Other values (82)20952.2%
 
(Missing)10526.2%
 
2020-12-12T11:02:21.914089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique34 ?
Unique (%)11.5%
2020-12-12T11:02:22.139873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length3.905
Min length2

rc
Categorical

MISSING

Distinct49
Distinct (%)18.1%
Missing130
Missing (%)32.5%
Memory size3.1 KiB
5.2
 
18
4.5
 
16
4.9
 
14
4.7
 
11
4.8
 
10
Other values (44)
201 
ValueCountFrequency (%) 
5.2184.5%
 
4.5164.0%
 
4.9143.5%
 
4.7112.8%
 
4.8102.5%
 
3.9102.5%
 
3.492.2%
 
4.692.2%
 
5.082.0%
 
6.182.0%
 
Other values (39)15739.2%
 
(Missing)13032.5%
 
2020-12-12T11:02:22.386180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)1.9%
2020-12-12T11:02:22.636058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.9675
Min length1

htn
Boolean

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size3.1 KiB
no
251 
yes
147 
(Missing)
 
2
ValueCountFrequency (%) 
no25162.7%
 
yes14736.8%
 
(Missing)20.5%
 
2020-12-12T11:02:22.794462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

dm
Categorical

Distinct5
Distinct (%)1.3%
Missing2
Missing (%)0.5%
Memory size3.1 KiB
no
258 
yes
134 
no
 
3
yes
 
2
yes
 
1
ValueCountFrequency (%) 
no25864.5%
 
yes13433.5%
 
no30.8%
 
yes20.5%
 
yes10.2%
 
(Missing)20.5%
 
2020-12-12T11:02:22.935851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.3%
2020-12-12T11:02:23.077171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:23.452421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.3625
Min length2

cad
Categorical

Distinct3
Distinct (%)0.8%
Missing2
Missing (%)0.5%
Memory size3.1 KiB
no
362 
yes
 
34
no
 
2
ValueCountFrequency (%) 
no36290.5%
 
yes348.5%
 
no20.5%
 
(Missing)20.5%
 
2020-12-12T11:02:23.859266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T11:02:24.015987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:24.274574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.095
Min length2

appet
Categorical

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.1 KiB
good
317 
poor
82 
ValueCountFrequency (%) 
good31779.2%
 
poor8220.5%
 
(Missing)10.2%
 
2020-12-12T11:02:24.566133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T11:02:24.785208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:24.923342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.9975
Min length3

pe
Boolean

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.1 KiB
no
323 
yes
76 
(Missing)
 
1
ValueCountFrequency (%) 
no32380.8%
 
yes7619.0%
 
(Missing)10.2%
 
2020-12-12T11:02:25.065348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

ane
Boolean

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.1 KiB
no
339 
yes
60 
(Missing)
 
1
ValueCountFrequency (%) 
no33984.8%
 
yes6015.0%
 
(Missing)10.2%
 
2020-12-12T11:02:25.143455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

classification
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
ckd
248 
notckd
150 
ckd
 
2
ValueCountFrequency (%) 
ckd24862.0%
 
notckd15037.5%
 
ckd 20.5%
 
2020-12-12T11:02:25.289344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T11:02:25.401776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:25.601595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length4.13
Min length3

Interactions

2020-12-12T11:01:36.062364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:36.351872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:36.594608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:36.819855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:37.056202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:37.275596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:37.476348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:37.739074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:38.047586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:38.344980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:38.537162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:38.777667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:39.040628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:39.331221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:39.644866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:39.820045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:40.071338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:40.243085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:40.435679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:40.646873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:40.819486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:40.985243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:41.130365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:41.417755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:41.630130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:41.852466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:42.068666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:42.251117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:42.619992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:42.818301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:42.983075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:43.167725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:43.371738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:43.551174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:43.800681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:43.978633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:44.220718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:44.599233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:44.817900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:45.045834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:45.286070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:45.526069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:45.720698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:45.945271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:46.150988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:46.352447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:46.553983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:46.774070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:46.993865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:47.206243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:47.387489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:47.608598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:47.853057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:48.106979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:48.319473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:48.716113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:48.948335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:49.178677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:49.518625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:49.759754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:49.997401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:50.210675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:50.380672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:50.575876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:50.770793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:50.980719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:51.177880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:51.352632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:51.546754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:51.732446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:51.900187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:52.074319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:52.256191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:52.448427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:52.605931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:52.810896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:53.032761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:53.234616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:53.451032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:53.628225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:53.810910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:53.980670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:54.250642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:54.554232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:54.934594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:55.130685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:55.386267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:55.565585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:55.785216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:56.026079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:56.218899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:56.417518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:56.606093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:56.798352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:56.993904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:57.170276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:57.384480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:57.581390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:57.754397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:57.944767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:58.234933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:58.474466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:58.663412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:58.836814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:59.028495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:59.253104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:59.568271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:59.763323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:01:59.972275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:00.169403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:00.335548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:00.514768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:00.708781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:01.178022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:01.436020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:01.676320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:01.953499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:02.184330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:02.413120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:02.704844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:02.979992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:03.180417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:03.360182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:03.586584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:03.782167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:04.230975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:04.580731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:04.846683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:05.051867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:05.395168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:05.619470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:05.808656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:06.011763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:06.355484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:06.554023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:06.743758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:06.962087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:07.175745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:07.363322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:07.565688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:07.750433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:07.946517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:08.307846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:08.472500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-12T11:02:25.820300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T11:02:26.165808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T11:02:26.495791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T11:02:26.917361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T11:02:27.388952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T11:02:09.085169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:10.543135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:11.335733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-12T11:02:12.234559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

idagebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
0048.080.01.0201.00.0NaNnormalnotpresentnotpresent121.036.01.2NaNNaN15.44478005.2yesyesnogoodnonockd
117.050.01.0204.00.0NaNnormalnotpresentnotpresentNaN18.00.8NaNNaN11.3386000NaNnononogoodnonockd
2262.080.01.0102.03.0normalnormalnotpresentnotpresent423.053.01.8NaNNaN9.6317500NaNnoyesnopoornoyesckd
3348.070.01.0054.00.0normalabnormalpresentnotpresent117.056.03.8111.02.511.23267003.9yesnonopooryesyesckd
4451.080.01.0102.00.0normalnormalnotpresentnotpresent106.026.01.4NaNNaN11.63573004.6nononogoodnonockd
5560.090.01.0153.00.0NaNNaNnotpresentnotpresent74.025.01.1142.03.212.23978004.4yesyesnogoodyesnockd
6668.070.01.0100.00.0NaNnormalnotpresentnotpresent100.054.024.0104.04.012.436NaNNaNnononogoodnonockd
7724.0NaN1.0152.04.0normalabnormalnotpresentnotpresent410.031.01.1NaNNaN12.44469005noyesnogoodyesnockd
8852.0100.01.0153.00.0normalabnormalpresentnotpresent138.060.01.9NaNNaN10.83396004.0yesyesnogoodnoyesckd
9953.090.01.0202.00.0abnormalabnormalpresentnotpresent70.0107.07.2114.03.79.529121003.7yesyesnopoornoyesckd

Last rows

idagebpsgalsurbcpcpccbabgrbuscsodpothemopcvwcrchtndmcadappetpeaneclassification
39039052.080.01.0250.00.0normalnormalnotpresentnotpresent99.025.00.8135.03.715.05263005.3nononogoodnononotckd
39139136.080.01.0250.00.0normalnormalnotpresentnotpresent85.016.01.1142.04.115.64458006.3nononogoodnononotckd
39239257.080.01.0200.00.0normalnormalnotpresentnotpresent133.048.01.2147.04.314.84666005.5nononogoodnononotckd
39339343.060.01.0250.00.0normalnormalnotpresentnotpresent117.045.00.7141.04.413.05474005.4nononogoodnononotckd
39439450.080.01.0200.00.0normalnormalnotpresentnotpresent137.046.00.8139.05.014.14595004.6nononogoodnononotckd
39539555.080.01.0200.00.0normalnormalnotpresentnotpresent140.049.00.5150.04.915.74767004.9nononogoodnononotckd
39639642.070.01.0250.00.0normalnormalnotpresentnotpresent75.031.01.2141.03.516.55478006.2nononogoodnononotckd
39739712.080.01.0200.00.0normalnormalnotpresentnotpresent100.026.00.6137.04.415.84966005.4nononogoodnononotckd
39839817.060.01.0250.00.0normalnormalnotpresentnotpresent114.050.01.0135.04.914.25172005.9nononogoodnononotckd
39939958.080.01.0250.00.0normalnormalnotpresentnotpresent131.018.01.1141.03.515.85368006.1nononogoodnononotckd